Abstract
The issue of devising efficient and effective solutions for supporting the analysis of process logs has recently received great attention from the research community, as effectively accomplishing any business process management task requires understanding the behavior of the processes. In this paper, we propose a new framework supporting the analysis of process logs, exhibiting two main features: a flexible data model (enabling an exhaustive representation of the facets of the business processes that are typically of interest for the analysis) and a graphical query language, providing a user-friendly tool for easily expressing both selection and aggregate queries over the business processes and the activities they are composed of. The framework can be easily and efficiently implemented by leveraging either “traditional” relational DBMSs or “innovative” NoSQL DBMSs, such as Neo4J.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Casati, F., Castellanos, M., Dayal, U., Salazar, N.: A generic solution for warehousing business process data. In: Proceedings of VLDB, pp. 1128–1137 (2007)
Deutch, D., Milo, T.: Type inference and type checking for queries over execution traces. VLDB J. 21(1), 51–68 (2012)
Gao, X.: Towards the next generation intelligent BPM – in the era of big data. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 4–9. Springer, Heidelberg (2013)
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.-C.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)
Ribeiro, J.T.S., Weijters, A.J.M.M.: Event cube: another perspective on business processes. In: Meersman, R., Dillon, T., Herrero, P., Kumar, A., Reichert, M., Qing, L., Ooi, B.-C., Damiani, E., Schmidt, D.C., White, J., Hauswirth, M., Hitzler, P., Mohania, M. (eds.) OTM 2011, Part I. LNCS, vol. 7044, pp. 274–283. Springer, Heidelberg (2011)
Schiefer, J., List, B., Bruckner, R.M.: Process data store: a real-time data store for monitoring business processes. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 760–770. Springer, Heidelberg (2003)
van der Aalst, W.M.P.: Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. In: Song, M., Wynn, M.T., Liu, J. (eds.) AP-BPM 2013. LNBIP, vol. 159, pp. 1–22. Springer, Heidelberg (2013)
van der Aalst, W.M.P., de Beer, H.T., van Dongen, B.F.: Process mining and verification of properties: an approach based on temporal logic. In: Meersman, R., Tari, Z. (eds.) OTM 2005. LNCS, vol. 3760, pp. 130–147. Springer, Heidelberg (2005)
van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., (Ton) Weijters, A.J.M.M.: Workflow mining: a survey of issues and approaches. Data & Knowl. Eng. 47(2), 237–267 (2003)
van der Aalst, W.M.P.: A decade of business process management conferences: personal reflections on a developing discipline. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 1–16. Springer, Heidelberg (2012)
Wang, S., Lv, C., Wen, L., Wang, J.: Managing massive business process models and instances with process space. In: Business Process Management Demos, p. 91 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fazzinga, B., Flesca, S., Furfaro, F., Masciari, E., Pontieri, L., Pulice, C. (2015). A Framework Supporting the Analysis of Process Logs Stored in Either Relational or NoSQL DBMSs. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-25252-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25251-3
Online ISBN: 978-3-319-25252-0
eBook Packages: Computer ScienceComputer Science (R0)